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Neural Networks for Target Selection in Direct Marketing

Author

Listed:
  • Potharst, R.
  • Kaymak, U.
  • Pijls, W.H.L.M.

Abstract

Partly due to a growing interest in direct marketing, it has become an important application field for data mining. Many techniques have been applied to select the targets in commercial applications, such as statistical regression, regression trees, neural computing, fuzzy clustering and association rules. Modeling of charity donations has also recently been considered. The availability of a large number of techniques for analyzing the data may look overwhelming and ultimately unnecessary at first. However, the amount of data used in direct marketing is tremendous. Further, there are different types of data and likely strong nonlinear relations amongst different groups within the data. Therefore, it is unlikely that there will be a single method that can be used under all circumstances. For that reason, it is important to have access to a range of different target selection methods that can be used in a complementary fashion. In this respect, learning systems such as neural networks have the advantage that they can adapt to the nonlinearity in the data to capture the complex relations. This is an important motivation for applying neural networks for target selection. In this report, neural networks are applied to target selection in modeling of charity donations. Various stages of model building are described by using data from a large Dutch charity organization as a case. The results are compared with the results of more traditional methods for target selection such as logistic regression and CHAID.

Suggested Citation

  • Potharst, R. & Kaymak, U. & Pijls, W.H.L.M., 2001. "Neural Networks for Target Selection in Direct Marketing," ERIM Report Series Research in Management ERS-2001-14-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  • Handle: RePEc:ems:eureri:83
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    File URL: https://repub.eur.nl/pub/83/erimrs20010329140337.pdf
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    References listed on IDEAS

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    1. Jan Roelf Bult & Tom Wansbeek, 1995. "Optimal Selection for Direct Mail," Marketing Science, INFORMS, vol. 14(4), pages 378-394.
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    Cited by:

    1. Jonker, J-J. & Piersma, N. & Potharst, R., 2002. "Direct Mailing Decisions for a Dutch Fundraiser," ERIM Report Series Research in Management ERS-2002-111-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    2. Jonker, J-J. & Piersma, N. & Potharst, R., 2002. "Direct Mailing Decisions for a Dutch Fundraiser," Econometric Institute Research Papers ERS-2002-111-LIS, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Potharst, R. & van Rijthoven, M. & van Wezel, M.C., 2005. "Modeling brand choice using boosted and stacked neural networks," Econometric Institute Research Papers EI 2005-05, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    More about this item

    Keywords

    data mining; direct mail; direct marketing; neural networks; target selection;

    JEL classification:

    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics

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